Predicting Cotton Fibre Maturity by Using Artificial Neural Network

• Autorzy: Farooq A. , Sarwar M. I. , Ashraf M. A. , Iqbal D. , Hussain A. , Malik S.

Identyfikatory

DOI

10.1515/aut-2018-0024

• Języki publikacji: EN

Abstrakty

EN

Cotton fibre maturity is the measure of cotton’s secondary cell wall thickness. Both immature and over-mature fibres are undesirable in textile industry due to the various problems caused during different manufacturing processes. The determination of cotton fibre maturity is of vital importance and various methods and techniques have been devised to measure or calculate it. Artificial neural networks have the power to model the complex relationships between the input and output variables. Therefore, a model was developed for the prediction of cotton fibre maturity using the fibre characteristics. The results of predictive modelling showed that mean absolute error of 0.0491 was observed between the actual and predicted values, which show a high degree of accuracy for neural network modelling. Moreover, the importance of input variables was also defined.

Słowa kluczowe

EN

cotton fibre maturity; fibre fineness; artificial neural networks

PL

dojrzewanie bawełny; włókno; sztuczne sieci neuronowe

• Wydawca: Lodz University of Technology, Faculty of Material Technologies and Textile Design
• Czasopismo: Autex Research Journal
• Rocznik: 2018
• Tom: Vol. 18, no. 4
• Strony: 429--433
• Opis fizyczny: Bibliogr. 28 poz.

Twórcy

autor

Farooq A.

• Department of Fibre and Textile Technology, University of Agriculture, Faisalabad, Pakistan

autor

Sarwar M. I.

• Central Cotton Research Institute Multan, Pakistan. 061-9200340, Tel: [phone], Fax: [phone]

autor

Ashraf M. A.

• Department of Fibre and Textile Technology, University of Agriculture, Faisalabad, Pakistan

autor

Iqbal D.

• Central Cotton Research Institute Multan, Pakistan. 061-9200340

autor

Hussain A.

• University College of Textile Engineering, Bahauddin Zakariya University, Multan

autor

Malik S.

• Institute of Textile Machinery and High-Performance Material Technology, Technical University Dresden, Germany

Bibliografia

• [1] Hu, X.-P. and Y.-L. Hsieh. (1996). Crystalline structure of developing cotton fibers. Journal of Polymer Science Part B: Polymer Physics. 34(8): 1451-1459.
• [2] Hsieh, Y.L. 2007. Chemical structure and properties of cotton, in Cotton: Science and technology, Gordon S. and Y.L., Hsieh, Editors. Woodhead Publishing Limited: Cambridge. pp. 3-34.
• [3] Basra, A.S., and C.P., Malik. 1984. Development of the cotton fiber, in International review of cytology, G.H. Bourne and J.F. Danielli, Editors., Academic press, Inc.: London.pp. 65-113.
• [4] Hsieh, Y.L., X.P. Hu and A. Wang. 2000. Single Fiber Strength Variations of Developing Cotton Fibers-Strength and Structure of G. hirsutum and G. barbedense. Textile Research Journal. 70(8): 682-690.
• [5] Matic-Leigh, R., & D.A., Cauthen. (1994). Determining cotton fibre maturity by image analysis part I: Direct measurement of cotton fibre characteristics. Textile Research Journal. 64:534-544.
• [6] Warner, S. B. (1995). Maturity of cotton, fibre cross-section and linear density, Fibre Science.
• [7] Rieter. (2014). Retrieved from http://www.rieter.com/cz/rikipedia/articles/technology-ofshort-staple-spinning/raw-material-as-a-factor-influencing-spinning/fibre-fineness/fibre-maturity/
• [8] Thibodeaux, D.P. & J.P. Evans. (1986). Cotton fiber maturity by image analysis. Textile Research Journal, 56(2):130-139.
• [9] Adel, G., F. Faten & A. Radhia. (2011). Assessing cotton fibre maturity and fineness by image analysis. Journal of Engineered Fibres and Fabrics, 6, 50-60.
• [10] American Society of Testing Materials. (2012). Standard Test Method for Maturity of Cotton Fibers (Sodium Hydroxide Swelling and Polarized Light Procedures) (D1442-06) ASTM International, West Conshohocken, PA..USA.
• [11] Smith, B. (1991). A review of the relationship of cotton maturity and dyeability. Textile Research Journal, 61(3), 137-145.
• [12] Paudel, D.R., E.F. Hequet & N. Abidi. (2013). Evaluation of cotton fiber maturity measurements. Industrial crops and products, 45, 435-441.
• [13] Abidi, N., E. Hequet, L. Cabrales, J. Gannaway, T. Wilkins, & L.W. Wells. (2008). Evaluating cell wall structure and composition of developing cotton fibers using Fourier transform infrared spectroscopy and thermo-gravimetric analysis. Journal of applied polymer science, 107(1), 476-486.
• [14] Wartelle, L.H., J.M. Bradow, O. Hinojosa, A.B. Pepperman, G. Sassenrath-Cole & P. Dastoor. (1995). Quantitative cotton fiber maturity measurements by X-ray fluorescence spectroscopy and advanced fiber information system. Journal of agricultural and food chemistry, 43(5), 1219-1223.
• [15] Lord, E. & S.A. Heap. (1988). The origin and assessment of cotton fibre maturity. Int. Institute for Cotton, Technical Research Division, Manchester, England.
• [16] Naylor, G.R.S. (2001). Cotton maturity and fineness measurement using the Sirolan-Laserscan.
• [17] Montalvo Jr., J.G. (2005). Relationships between micronaire, fineness, and maturity. Part-I. Fundamentals. Journal of Cotton Science, 9, 81-88.
• [18] Gordon, S.G. & G.R.S. Naylor. (2004). Instrumentation for rapid direct measurement of cotton fibre fineness and maturity.
• [19] Erbil, Y., O. Babaarslan & İ.Ilhan. (2018). A comparative prediction for tensile properties of ternary blended open-end rotor yarns using regression and neural network models. The Journal of The Textile Institute, 109(4): 560-568.”
• [20] Kanat, Z.E. & N. Özdil. (2018). Application of artificial neural network (ANN) for the prediction of thermal resistance of knitted fabrics at different moisture content. The Journal of The Textile Institute, 1-7.
• [21] Mandhyan, P.K., R.P. Nachane, P.G. Patil, B.R. Pawar, H. Hasan & S.S. Venkatkrishnan. (2018). Influence of segregation of cotton bales based on its fiber attributes in yarn properties. Journal of Natural Fibers, 1-9.
• [22] Shiau, Y.R., I.S. Tsai & C.S. Lin. (2000). Classifying web defects with a back-propagation neural network by color image processing. Textile Research Journal 70:633-640.
• [23] Farooq, A., & C. Cherif. (2008). Use of artificial neural networks for determining the leveling action point at the auto-leveling draw frame.Textile Research Journal, 78, 502-509.
• [24] Huang, C.C., & K.T. Chang. (2001). Fuzzy self-organizing and neural network control of sliver linear density in a drawing frame. Textile Research Journal, 71, 987-992.
• [25] Cheng, L., & D.L., Adams. (1995). Yarn strength prediction using neural networks Part I: Fibre properties and yarn strength relationship. Textile Research Journal, 65, 495-500.
• [26] Sette, S., L., Boullart, L.V., Langenhove,& P., Kiekens. (1997). Optimizing the fiber-to-yarn production process with a combined neural network/genetic algorithm approach. Textile Research Journal, 67, 84-92.
• [27] Yang, S. & S. Gordon. (2018). Fiber-to-yarn predictions. In Engineering of High-Performance Textiles (pp. 81-106).
• [28] Turhan, Y., & O. Toprakci (2012). Comparison of high-volume instrument and advanced fiber information systems based on prediction performance of yarn properties using a radial basis function neural network. Textile Research Journal, 83, 130-147.